OmicsDiscovery is a web-based workbench that lets cancer researchers build, explore, and analyze transcriptomic atlases from cancer cell lines — without writing a single line of R code. Search across thousands of cell lines, run differential expression, visualize results with publication-ready plots, and download everything in one click.
Search across 70+ breast cancer cell lines in the built-in BC-84 atlas. Find cells by name (e.g. MCF7, T47D), subtype, or tissue of origin.
Search ~2,000 cancer cell lines from the Cancer Dependency Map. Filter by tissue type (lung, breast, blood, etc.). Fetch expression TPM data for any cell line.
Import any public RNA-seq dataset from NCBI GEO by accession number (e.g. GSE48216). Auto-detects log-scale data and converts to linear for consistent analysis.
Import CSV, XLSX, TSV, or TXT expression matrices. The tool auto-detects gene columns and cell line samples. Your data is normalized into a unified 4-sheet atlas format.
Visualize log₂ fold-change vs. mean expression. Blue = up-regulated, red = down-regulated, grey = not significant. Configurable log₂FC and adjusted p-value thresholds.
Plot statistical significance (-log₁₀ p-value) vs. magnitude of change (log₂FC). Quickly identify the most differentially expressed genes at a glance.
Cluster and visualize expression patterns across any number of cell lines. Uses ComplexHeatmap with variance-based top gene selection and z-score normalization.
Principal Component Analysis with variance explained per component. Visualize sample clustering and identify outliers in your dataset.
Comprehensive enrichment across 10 databases: GO (BP/MF/CC), KEGG, Reactome, WikiPathways, Disease Ontology, MSigDB, ChEA, KEA, plus GSEA and PPI network analysis.
11 configurable knobs: scale detection, deduplication, KNN imputation, gene/sample filtering, batch correction (ComBat), CPM+log₂ transform, DESeq2 size factors.
Download publication-ready PNG plots, CSV expression matrices, and multi-sheet Excel atlases (Expression_Matrix + Metadata + Gene_Annotations + Summary).
Save your complete session (selected cells, thresholds, results) to your browser. Load it later to resume exactly where you left off.
Four simple steps from data to publication-ready results.
Use the built-in BC-84 breast cancer atlas (70 cell lines), search DepMap for ~2,000 cancer cell lines, fetch any RNA-seq dataset from NCBI GEO, or upload your own expression file.
Click cell cards to select them. You can select as many as you need. Use "Select All" to select all available cells at once. Fetched cells are automatically selected for you.
Adjust log₂ fold-change threshold, adjusted p-value cutoff, and number of top genes using the sliders. Set your reference/control cell line (defaults to MCF10A).
Click any analysis button (MA, Volcano, Heatmap, PCA, GO, Normalize). Results appear in tabs with plots, summary statistics, and download buttons for PNG, CSV, and Excel.
The GO enrichment tool runs over-representation analysis (ORA) and gene set enrichment analysis (GSEA) across these databases simultaneously:
Files should have genes in rows and cell lines/samples in columns.
The first column should contain gene IDs or gene symbols (e.g. ENSG00000141510 or TP53).
Expression values can be raw counts, TPM, FPKM, or log₂-transformed — the tool auto-detects and converts.
All analyses use established R/Bioconductor packages that have been published in scientific journals:
DESeq2
Love et al. (2014) Genome Biology
clusterProfiler
Yu et al. (2012) OMICS
ComplexHeatmap
Gu et al. (2016) Bioinformatics
STRINGdb
Szklarczyk et al. (2023) NAR
ReactomePA
Yu & He (2016) Mol. BioSystems
GEOquery
Davis & Meltzer (2007) Bioinformatics
OmicsDiscovery · Interactive transcriptomic atlas builder & explorer
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